Every night, hundreds of thousands of tourists prefer to pay and stay in the property of a stranger, found online on Airbnb webstie instead of booking a traditional tourism accommodation such as a hotel. Since Airbnb 2008, proposed an online platform where people can rent mostly for tourism different type of properties: rooms, appartments, houses and sometimes more esoteric places. Over the past several years Airbnb has rapidly and massively grown to the point that today anyone can rent and find a spot virtually in any country or city of the world.
In this report we focus on Paris, capital of France, and will try to decipher some general tendencies regarding the prices proposed by parisian hosts. This analysis will be performed in the frame of four major objectives. Firstly we will try to uncover the features that impact on the price of property with a specific focus on appartment. Secondly we will focus more on parisian hosts and try to determine how many appartments a parisian commonly propose for renting. Thirdly we will take a geographical approach in trying to assess whether locations of the properties impact on prices. Finally we study and quantify the number of visits in the capital longitudinally based on the number of AirBnB renting.
R session details are provided at the end of this report as the ouput of the R sessionInfo() function. The main packages imported and used for this data analysis are: ggpubr_0.3.0, ggplot2_3.3.0, purrr_0.3.4, plyr_0.8.5, readr_1.3.1.
## [1] "L" "R"
The"AirBnB.Rdata" data set comes as two different tables named L and R.